OpenRaiser/Intern-Atlas huggingface dataset:
A Confidential Computing Transparency Framework for a Comprehensive Trust Chain
Venue: arXiv cs.CR 2024
Year: 2024

Confidential Computing enhances privacy of data in-use through hardware-based Trusted Execution Environments (TEEs) that use attestation to verify their integrity, authenticity, and certain runtime properties, along with those of the binaries they execute. However, TEEs require user trust, as attestation alone cannot guarantee the absence of vulnerabilities or backdoors. Enhanced transparency can mitigate the reliance on naive trust. Some organisations currently employ various transparency measures, including open-source firmware, publishing technical documentation, or undergoing external audits, but these require investments with unclear returns. This may discourage the adoption of transparency, leaving users with limited visibility into system privacy measures. Additionally, the lack of standardisation complicates meaningful comparisons between implementations. To address these challenges, we propose a three-level conceptual framework providing organisations with a practical pathway to incrementally improve Confidential Computing transparency. To evaluate whether our transparency framework contributes to an increase in end-user trust, we conducted an empirical study with over 800 non-expert participants. The results indicate that greater transparency improves user comfort, with participants willing to share various types of personal data across different levels of transparency. The study also reveals misconceptions about transparency, highlighting the need for clear communication and user education.
Differentiable Discrete Event Simulation for Queuing Network Control
Venue: arXiv cs.LG 2024
Year: 2024

Queuing network control is essential for managing congestion in job-processing systems such as service systems, communication networks, and manufacturing processes. Despite growing interest in applying reinforcement learning (RL) techniques, queueing network control poses distinct challenges, including high stochasticity, large state and action spaces, and lack of stability. To tackle these challenges, we propose a scalable framework for policy optimization based on differentiable discrete event simulation. Our main insight is that by implementing a well-designed smoothing technique for discrete event dynamics, we can compute pathwise policy gradients for large-scale queueing networks using auto-differentiation software (e.g., Tensorflow, PyTorch) and GPU parallelization. Through extensive empirical experiments, we observe that our policy gradient estimators are several orders of magnitude more accurate than typical REINFORCE-based estimators. In addition, We propose a new policy architecture, which drastically improves stability while maintaining the flexibility of neural-network policies. In a wide variety of scheduling and admission control tasks, we demonstrate that training control policies with pathwise gradients leads to a 50-1000x improvement in sample efficiency over state-of-the-art RL methods. Unlike prior tailored approaches to queueing, our methods can flexibly handle realistic scenarios, including systems operating in non-stationary environments and those with non-exponential interarrival/service times.
Post-quantum encryption algorithms of high-degree 3-variable polynomial congruences: BS cryptosystems and BS key generation
Venue: arXiv cs.CR 2024
Year: 2024

We will construct post-quantum encryption algorithms based on three-variable polynomial Beal-Schur congruence. After giving a proof of Beal's conjecture and citing some applications of it to selected cases where the discrete logarithm and some of its generalizations are unsolvable problems, we will investigate the formulation and validity of an appropriate version of the Beal's conjecture on finite fields of integers. In contrast to the infinite case, we will show that the corresponding Beal-Schur congruence equation $x^{p}+y^{q}\equiv z^{r} (mod \mathcal{N})$ has non-trivial solutions into the finite field $\mathbb{Z}_{\mathcal{N}} $, for all sufficiently large primes $\mathcal{N}$ that do not divide the product $xyz$, under certain mutual divisibility conditions of the exponents $p$, $q$ and $r$. We will apply this result to generate the so-called BS cryptosystems, i.e., simple and secure post-quantum encryption algorithms based on the Beal-Schur congruence equation, as well as new cryptographic key generation methods, whose post-quantum algorithmic encryption security relies on having an infinite number of options for the parameters $p$, $q$, $r$, $\mathcal{N}$.
Modeling Human Strategy for Flattening Wrinkled Cloth Using Neural Networks
Venue: arXiv cs.CV 2024
Year: 2024

This paper explores a novel approach to model strategies for flattening wrinkled cloth learning from humans. A human participant study was conducted where the participants were presented with various wrinkle types and tasked with flattening the cloth using the fewest actions possible. A camera and Aruco marker were used to capture images of the cloth and finger movements, respectively. The human strategies for flattening the cloth were modeled using a supervised regression neural network, where the cloth images served as input and the human actions as output. Before training the neural network, a series of image processing techniques were applied, followed by Principal Component Analysis (PCA) to extract relevant features from each image and reduce the input dimensionality. This reduction decreased the model's complexity and computational cost. The actions predicted by the neural network closely matched the actual human actions on an independent data set, demonstrating the effectiveness of neural networks in modeling human actions for flattening wrinkled cloth.
Distributed Path Compression for Piecewise Linear Morse-Smale Segmentations and Connected Components
Venue: arXiv cs.DC 2024
Year: 2024

This paper describes the adaptation of a well-scaling parallel algorithm for computing Morse-Smale segmentations based on path compression to a distributed computational setting. Additionally, we extend the algorithm to efficiently compute connected components in distributed structured and unstructured grids, based either on the connectivity of the underlying mesh or a feature mask. Our implementation is seamlessly integrated with the distributed extension of the Topology ToolKit (TTK), ensuring robust performance and scalability. To demonstrate the practicality and efficiency of our algorithms, we conducted a series of scaling experiments on large-scale datasets, with sizes of up to 4096^3 vertices on up to 64 nodes and 768 cores.
BreachSeek: A Multi-Agent Automated Penetration Tester
Venue: arXiv cs.CR 2024
Year: 2024

The increasing complexity and scale of modern digital environments have exposed significant gaps in traditional cybersecurity penetration testing methods, which are often time-consuming, labor-intensive, and unable to rapidly adapt to emerging threats. There is a critical need for an automated solution that can efficiently identify and exploit vulnerabilities across diverse systems without extensive human intervention. BreachSeek addresses this challenge by providing an AI-driven multi-agent software platform that leverages Large Language Models (LLMs) integrated through LangChain and LangGraph in Python. This system enables autonomous agents to conduct thorough penetration testing by identifying vulnerabilities, simulating a variety of cyberattacks, executing exploits, and generating comprehensive security reports. In preliminary evaluations, BreachSeek successfully exploited vulnerabilities in exploitable machines within local networks, demonstrating its practical effectiveness. Future developments aim to expand its capabilities, positioning it as an indispensable tool for cybersecurity professionals.
Users' Perspectives on Multimodal Menstrual Tracking Using Consumer Health Devices
Venue: arXiv cs.HC 2024
Year: 2024

Previous menstrual health literature highlights a variety of signals not included in existing menstrual trackers because they are either difficult to gather or are not typically associated with menstrual health. Since it has become increasingly convenient to collect biomarkers through wearables and other consumer-grade devices, our work examines how people incorporate unconventional signals (e.g., blood glucose levels, heart rate) into their understanding of menstrual health. In this paper, we describe a three-month-long study on fifty participants' experiences as they tracked their health using physiological sensors and daily diaries. We analyzed their experiences with both conventional and unconventional menstrual health signals through surveys and interviews conducted throughout the study. We delve into the various aspects of menstrual health that participants sought to affirm using unconventional signals, explore how these signals influenced their daily behaviors, and examine how multimodal menstrual tracking expanded their scope of menstrual health. Finally, we provide design recommendations for future multimodal menstrual trackers.
Cost-Control in Display Advertising: Theory vs Practice
Venue: arXiv cs.GT 2024
Year: 2024

In display advertising, advertisers want to achieve a marketing objective with constraints on budget and cost-per-outcome. This is usually formulated as an optimization problem that maximizes the total utility under constraints. The optimization is carried out in an online fashion in the dual space - for an incoming Ad auction, a bid is placed using an optimal bidding formula, assuming optimal values for the dual variables; based on the outcome of the previous auctions, the dual variables are updated in an online fashion. While this approach is theoretically sound, in practice, the dual variables are not optimal from the beginning, but rather converge over time. Specifically, for the cost-constraint, the convergence is asymptotic. As a result, we find that cost-control is ineffective. In this work, we analyse the shortcomings of the optimal bidding formula and propose a modification that deviates from the theoretical derivation. We simulate various practical scenarios and study the cost-control behaviors of the two algorithms. Through a large-scale evaluation on the real-word data, we show that the proposed modification reduces the cost violations by 50%, thereby achieving a better cost-control than the theoretical bidding formula.
WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking
Venue: arXiv cs.LG 2024
Year: 2024

Nowadays, deep neural networks are used for solving complex tasks in several critical applications and protecting both their integrity and intellectual property rights (IPR) has become of utmost importance. To this end, we advance WaterMAS, a substitutive, white-box neural network watermarking method that improves the trade-off among robustness, imperceptibility, and computational complexity, while making provisions for increased data payload and security. WasterMAS insertion keeps unchanged the watermarked weights while sharpening their underlying gradient space. The robustness is thus ensured by limiting the attack's strength: even small alterations of the watermarked weights would impact the model's performance. The imperceptibility is ensured by inserting the watermark during the training process. The relationship among the WaterMAS data payload, imperceptibility, and robustness properties is discussed. The secret key is represented by the positions of the weights conveying the watermark, randomly chosen through multiple layers of the model. The security is evaluated by investigating the case in which an attacker would intercept the key. The experimental validations consider 5 models and 2 tasks (VGG16, ResNet18, MobileNetV3, SwinT for CIFAR10 image classification, and DeepLabV3 for Cityscapes image segmentation) as well as 4 types of attacks (Gaussian noise addition, pruning, fine-tuning, and quantization). The code will be released open-source upon acceptance of the article.
The Role of Generative Systems in Historical Photography Management: A Case Study on Catalan Archives
Venue: arXiv cs.CV 2024
Year: 2024

The use of image analysis in automated photography management is an increasing trend in heritage institutions. Such tools alleviate the human cost associated with the manual and expensive annotation of new data sources while facilitating fast access to the citizenship through online indexes and search engines. However, available tagging and description tools are usually designed around modern photographs in English, neglecting historical corpora in minoritized languages, each of which exhibits intrinsic particularities. The primary objective of this research is to study the quantitative contribution of generative systems in the description of historical sources. This is done by contextualizing the task of captioning historical photographs from the Catalan archives as a case study. Our findings provide practitioners with tools and directions on transfer learning for captioning models based on visual adaptation and linguistic proximity.
Image Recognition for Garbage Classification Based on Pixel Distribution Learning
Venue: arXiv cs.CV 2024
Year: 2024

The exponential growth in waste production due to rapid economic and industrial development necessitates efficient waste management strategies to mitigate environmental pollution and resource depletion. Leveraging advancements in computer vision, this study proposes a novel approach inspired by pixel distribution learning techniques to enhance automated garbage classification. The method aims to address limitations of conventional convolutional neural network (CNN)-based approaches, including computational complexity and vulnerability to image variations. We will conduct experiments using the Kaggle Garbage Classification dataset, comparing our approach with existing models to demonstrate the strength and efficiency of pixel distribution learning in automated garbage classification technologies.
A Survey on Signed Graph Embedding: Methods and Applications
Venue: arXiv cs.SI 2024
Year: 2024

A signed graph (SG) is a graph where edges carry sign information attached to it. The sign of a network can be positive, negative, or neutral. A signed network is ubiquitous in a real-world network like social networks, citation networks, and various technical networks. There are many network embedding models have been proposed and developed for signed networks for both homogeneous and heterogeneous types. SG embedding learns low-dimensional vector representations for nodes of a network, which helps to do many network analysis tasks such as link prediction, node classification, and community detection. In this survey, we perform a comprehensive study of SG embedding methods and applications. We introduce here the basic theories and methods of SGs and survey the current state of the art of signed graph embedding methods. In addition, we explore the applications of different types of SG embedding methods in real-world scenarios. As an application, we have explored the citation network to analyze authorship networks. We also provide source code and datasets to give future direction. Lastly, we explore the challenges of SG embedding and forecast various future research directions in this field.
RETAIN: Interactive Tool for Regression Testing Guided LLM Migration
Venue: arXiv cs.IR 2024
Year: 2024

Large Language Models (LLMs) are increasingly integrated into diverse applications. The rapid evolution of LLMs presents opportunities for developers to enhance applications continuously. However, this constant adaptation can also lead to performance regressions during model migrations. While several interactive tools have been proposed to streamline the complexity of prompt engineering, few address the specific requirements of regression testing for LLM Migrations. To bridge this gap, we introduce RETAIN (REgression Testing guided LLM migrAtIoN), a tool designed explicitly for regression testing in LLM Migrations. RETAIN comprises two key components: an interactive interface tailored to regression testing needs during LLM migrations, and an error discovery module that facilitates understanding of differences in model behaviors. The error discovery module generates textual descriptions of various errors or differences between model outputs, providing actionable insights for prompt refinement. Our automatic evaluation and empirical user studies demonstrate that RETAIN, when compared to manual evaluation, enabled participants to identify twice as many errors, facilitated experimentation with 75% more prompts, and achieves 12% higher metric scores in a given time frame.
Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learning
Venue: arXiv cs.CV 2024
Year: 2024

This paper proposes a method for unsupervised whole-image clustering of a target dataset of remote sensing scenes with no labels. The method consists of three main steps: (1) finetuning a pretrained deep neural network (DINOv2) on a labelled source remote sensing imagery dataset and using it to extract a feature vector from each image in the target dataset, (2) reducing the dimension of these deep features via manifold projection into a low-dimensional Euclidean space, and (3) clustering the embedded features using a Bayesian nonparametric technique to infer the number and membership of clusters simultaneously. The method takes advantage of heterogeneous transfer learning to cluster unseen data with different feature and label distributions. We demonstrate the performance of this approach outperforming state-of-the-art zero-shot classification methods on several remote sensing scene classification datasets.
Epistemic Uncertainty and Observation Noise with the Neural Tangent Kernel
Venue: arXiv cs.LG 2024
Year: 2024

Recent work has shown that training wide neural networks with gradient descent is formally equivalent to computing the mean of the posterior distribution in a Gaussian Process (GP) with the Neural Tangent Kernel (NTK) as the prior covariance and zero aleatoric noise \parencite{jacot2018neural}. In this paper, we extend this framework in two ways. First, we show how to deal with non-zero aleatoric noise. Second, we derive an estimator for the posterior covariance, giving us a handle on epistemic uncertainty. Our proposed approach integrates seamlessly with standard training pipelines, as it involves training a small number of additional predictors using gradient descent on a mean squared error loss. We demonstrate the proof-of-concept of our method through empirical evaluation on synthetic regression.
On the Prevalence, Evolution, and Impact of Code Smells in Simulation Modelling Software
Venue: arXiv cs.SE 2024
Year: 2024

Simulation modelling systems are routinely used to test or understand real-world scenarios in a controlled setting. They have found numerous applications in scientific research, engineering, and industrial operations. Due to their complex nature, the simulation systems could suffer from various code quality issues and technical debt. However, to date, there has not been any investigation into their code quality issues (e.g. code smells). In this paper, we conduct an empirical study investigating the prevalence, evolution, and impact of code smells in simulation software systems. First, we employ static analysis tools (e.g. Designite) to detect and quantify the prevalence of various code smells in 155 simulation and 327 traditional projects from Github. Our findings reveal that certain code smells (e.g. Long Statement, Magic Number) are more prevalent in simulation software systems than in traditional software systems. Second, we analyze the evolution of these code smells across multiple project versions and investigate their chances of survival. Our experiments show that some code smells such as Magic Number and Long Parameter List can survive a long time in simulation software systems. Finally, we examine any association between software bugs and code smells. Our experiments show that although Design and Architecture code smells are introduced simultaneously with bugs, there is no significant association between code smells and bugs in simulation systems.
DECAN: A Denoising Encoder via Contrastive Alignment Network for Dry Electrode EEG Emotion Recognition
Venue: arXiv cs.HC 2024
Year: 2024

EEG signal is important for brain-computer interfaces (BCI). Nevertheless, existing dry and wet electrodes are difficult to balance between high signal-to-noise ratio and portability in EEG recording, which limits the practical use of BCI. In this study, we propose a Denoising Encoder via Contrastive Alignment Network (DECAN) for dry electrode EEG, under the assumption of the EEG representation consistency between wet and dry electrodes during the same task. Specifically, DECAN employs two parameter-sharing deep neural networks to extract task-relevant representations of dry and wet electrode signals, and then integrates a representation-consistent contrastive loss to minimize the distance between representations from the same timestamp and category but different devices. To assess the feasibility of our approach, we construct an emotion dataset consisting of paired dry and wet electrode EEG signals from 16 subjects with 5 emotions, named PaDWEED. Results on PaDWEED show that DECAN achieves an average accuracy increase of 6.94$\%$ comparing to state-of-the art performance in emotion recognition of dry electrodes. Ablation studies demonstrate a decrease in inter-class aliasing along with noteworthy accuracy enhancements in the delta and beta frequency bands. Moreover, an inter-subject feature alignment can obtain an accuracy improvement of 5.99$\%$ and 5.14$\%$ in intra- and inter-dataset scenarios, respectively. Our proposed method may open up new avenues for BCI with dry electrodes. PaDWEED dataset used in this study is freely available at https://huggingface.co/datasets/peiyu999/PaDWEED.
Design and Characterization of MRI-compatible Plastic Ultrasonic Motor
Venue: arXiv cs.RO 2024
Year: 2024

Precise surgical procedures may benefit from intra-operative image guidance using magnetic resonance imaging (MRI). However, the MRI's strong magnetic fields, fast switching gradients, and constrained space pose the need for an MR-guided robotic system to assist the surgeon. Piezoelectric actuators can be used in an MRI environment by utilizing the inverse piezoelectric effect for different application purposes. Piezoelectric ultrasonic motor (USM) is one type of MRI-compatible actuator that can actuate these robots with fast response times, compactness, and simple configuration. Although the piezoelectric motors are mostly made of nonferromagnetic material, the generation of eddy currents due to the MRI's gradient fields can lead to magnetic field distortions causing image artifacts. Motor vibrations due to interactions between the MRI's magnetic fields and those generated by the eddy currents can further degrade image quality by causing image artifacts. In this work, a plastic piezoelectric ultrasonic (USM) motor with more degree of MRI compatibility was developed and induced with preliminary optimization. Multiple parameters, namely teeth number, notch size, edge bevel or straight, and surface finish level parameters were used versus the prepressure for the experiment, and the results suggested that using 48 teeth, thin teeth notch with 0.39mm, beveled edge and a surface finish using grit number of approximate 1000 sandpaper performed a better output both in rotary speed and torque. Under this combination, the highest speed reached up to 436.6665rpm when the prepressure was low, and the highest torque reached up to 0.0348Nm when the prepressure was approximately 500g.